Abstract
This paper is aiming at the problem of low tracking accuracy of moving vehicles in fast traffic scenarios, the ECO algorithm based on correlation filtering is applied to real-time tracking to achieve stable and accurate tracking of target vehicles. Through the factorized convolution operator, the extracted features are more comprehensive and efficient; the optimization of the training set effectively reduces redundancy; and the more representative correlation filter is used to prevent over-fitting; a simple update model is used to prevent model drift. The experimental results show that the proposed method finally achieved a tracking effect of 97% recognition rate.
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